基于多属性决策树的电网暂态稳定规则提取方法
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摘要
随着清洁能源接入和交直流混联电网规模的不断扩大,电网调度运行方式更为复杂、多变,基于"在线计算辅助决策+人工经验判断"的调度控制方式已难以满足当前电网需求。提出一种基于暂态稳定裕度指标的多属性决策树方法,通过计算互信息对原始电气量进行初步分析,筛选出与系统稳定性关联度较高的特征属性;进而利用线性判别分析方法对所选属性进行最佳投影,获得各决策属性的组合特征;通过暂态稳定裕度指标离散化和样本训练生成决策树,得到可评估预想故障下系统稳定性的通用规则,并利用对决策树的回推分析获得可提高系统稳定性的运行方式调整策略;最后利用IEEE-39节点算例的仿真与分析验证了所提出方法的正确性和有效性。
With the development of the renewable energy generation and the AC-DC hybrid interconnected power grid, the operation and dispatching mode of the power grid is more complex and changeable. The traditional dispatch and control method based on online computer-assisted decision support and artificial experience is no longer suitable for the up-to-date power grid. In this paper, a multiattribute decision tree is proposed based on transient stability margin index. The mutual information is used to primarily analyze the electrical variables. Then linear discriminant analysis is applied to get the best the projection of the selected attributes, from which the combination feature of attributes can be get.The decision tree is constructed after the discretization of the transient stability margins under some specified faults, then the general rules for evaluating the stability of the system are achieved. The operation mode adjustment strategies for improving the system stability are obtained through the backward analysis of the decision tree. Finally, the IEEE-39 node test system is used to verify the correctness and effectiveness of the proposed method.
With the development of the renewable energy generation and the AC-DC hybrid interconnected power grid, the operation and dispatching mode of the power grid is more complex and changeable. The traditional dispatch and control method based on online computer-assisted decision support and artificial experience is no longer suitable for the up-to-date power grid. In this paper, a multiattribute decision tree is proposed based on transient stability margin index. The mutual information is used to primarily analyze the electrical variables. Then linear discriminant analysis is applied to get the best the projection of the selected attributes, from which the combination feature of attributes can be get.The decision tree is constructed after the discretization of the transient stability margins under some specified faults, then the general rules for evaluating the stability of the system are achieved. The operation mode adjustment strategies for improving the system stability are obtained through the backward analysis of the decision tree. Finally, the IEEE-39 node test system is used to verify the correctness and effectiveness of the proposed method.
引文
[1]于之虹,黄彦浩,鲁广明,等.基于时间序列关联分析的稳定运行规则提取方法[J].中国电机工程学报,2015,35(3):519-526.Yu Zhihong,Huang Yanhao,Lu Guangming,et al.Atime series associative classification method for the operation rule extracting of transient stability[J].Proceedings of the CSEE,2015,35(3):519-526.
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[13]叶圣永,王晓茹,刘志刚,等.基于支持向量机增量学习的电力系统暂态稳定评估[J].电力系统自动化.2011,35(11):15-19.Ye Shengyong,Wang Xiaoru,Liu Zhigang,et al.Power system transient stability assessment base on support vector machine incremental learning method[J].Automation of Electric Power Systems,2011,35(11):15-19.
[14]Zheng C,Malbasa V,Kezunovic M.Regression tree for stability margin prediction using synchrophasor meas-urements[J].IEEE Transactions on Power Systems,2013,28(2):1978-1987.
[15]Rovnyak S,Kretsinger S,Thorp J,et al.Decision trees for real-time transient stability prediction[J].IEEETransactions on Power Systems,1994,9(3):1417-1426.
[16]Diao R,Vittal V,Logic N.Design of a real-time security assessment tool for situational awareness enhancement in modern power systems[J].IEEETransactions on Power Systems,2010,25(2):957-965
[17]王康,孙宏斌,张伯明,等.基于二维组合属性决策树的暂态稳定评估[J].中国电机工程学报,2009,29(增刊1):17-24.Wang Kang,Sun Hongbin,Zhang Boming,et al.Transient stability assessment based on 2D combined at-tribute decision tree[J].Proceedings of the CSEE,2009,29(S1):17-24.
[18]Xu Yan,Dong Zhaoyang,Zhang Rui,et al.A decision tree-based on-line preventive control strategy for power system transient instability prevention[J].International Journal of Systems Science,2014,45(2):176-186.
[19]Genc I,Diao R,Vittal V,et al.Decision tree-based preventive and corrective control applications for dynamic security enhancement in power systems[J].IEEETransactions on Power Systems,2010,25(3):1611-1619.
[20]Gao Qun,Rovnyak S M.Decision trees using synchronized phasor measurements for wide-area response-based control[J].IEEE Transactions on Power Systems,2011,26(2):855-861.
[21]孙宏斌,王康,张伯明,等.采用线性决策树的暂态稳定规则提取[J].中国电机工程学报.2011,31(34):61-67.Sun Hongbin,Wang Kang,Zhang Boming,et al.Rule extraction in transient stability study using linear decision trees[J].Proceedings of the CSEE,2011,31(34):61-67.
[22]He Miao,Zhang Junshan,Vittal V.Robust online dynamic security assessment using adaptive ensemble decision-tree learning[J].IEEE Transactions on Power Systems,2013,28(4):4089-4098.
[23]Han Jiawei,Kamber M,Pei Jian.数据挖掘概念与技术[M].范明,孟小峰,译.北京:机械工业出版社,2012.
[24]Welling M.Fisher linear discriminant analysis[J]Department of Computer Science,2009,16(94):237-280.
[25]孙静.面向情报大数据的决策树增量学习算法研究[D].哈尔滨:哈尔滨工程大学,2017.
[26]Hiskens I.IEEE PES task force on benchmark systems for stability controls[R].Technical Report,2013.
[27]黄彦浩,于之虹,史东宇,等.基于海量在线历史数据的大电网快速判稳策略[J].中国电机工程学报,2016,36(3):596-603.Huang Yanhao,Yu Zhihong,Shi Dongyu,et al.Strategy of huge electric power system stability quick judgment based on massive historical online data[J].Proceedings of the CSEE,2016,36(3):596-603.
[28]韦肖燕,李欣然,钱军,等.采用储能电源辅助的暂态稳定紧急控制方法[J].电工技术学报,2017,32(18):286-300.Wei Xiaoyan,Li Xinran,Qian Jun,et al.Power system transient stability emergency control method assisted by energy storage[J].Transactions of China Electrotechnical Society,2017,32(18):286-300.
[29]唐飞,贾骏,刘涤尘,等.一种考虑发电机同调分群的大电网快速主动解列策略[J].电工技术学报,2016,31(17):32-40.Tang Fei,Jia Jun,Liu Dichen,et al.A fast active islanding scheme considering generators'coherent partition[J].Transactions of China Electrotechnical Society,2016,31(17):32-40.
[2]陈厚合,王长江,姜涛,等.基于投影能量函数和Pin-SVM的电力系统暂态稳定评估[J].电工技术学报,2017,32(11):67-76.Chen Houhe,Wang Changjiang,Jiang Tao,et al.Transient stability assessment in bulk power grid using projection energy function and support vector machine with pinball loss[J].Transactions of China Electrotechnical Society,2017,32(11):67-76.
[3]赵晋泉,邓晖,吴小辰,等.基于广域响应的电力系统暂态稳定控制技术评述[J].电力系统保护与控制,2016,44(5):1-9.Zhao Jinquan,Deng Hui,Wu Xiaochen,et al.Review on power system transient stability control technologies based on PMU/WAMS[J].Power System Protection and Control,2016,44(5):1-9.
[4]沈鑫,束洪春,曹敏,等.大区互联电网的动态稳定风险评估研究与应用[J].电工技术学报,2016,31(增刊1):230-238.Shen Xin,Shu Hongchun,Cao Min,et al.Risk assessment and research of dynamic stability for large-scale interconnected grids and its application[J].Transactions of China Electrotechnical Society,2016,31(S1):230-238.
[5]刘书君,杨虎,安学利.基于一类新的预估-校正策略的电力系统暂态稳定快速仿真算法[J].电力系统保护与控制,2017,45(14):32-37.Liu Shujun,Yang Hu,An Xueli.Fast simulation algorithm for power system transient stability based on a new predictor-corrector strategy[J].Power System Protection and Control,2017,45(14):32-37.
[6]李扬,李国庆,顾雪平,等.基于集成OS-ELM的暂态稳定评估方法[J].电工技术学报,2015,30(14):412-418.Li Yang,Li Guoqing,Gu Xueping,et al.Transient stability assessment of power systems based on ensemble OS-ELM[J].Transactions of China Electrotechnical Society,2015,30(14):412-418.
[7]田芳,周孝信,于之虹.基于支持向量机综合分类模型和关键样本集的电力系统暂态稳定评估[J].电力系统保护与控制,2017,45(22):1-8.Tian Fang,Zhou Xiaoxin,Yu Zhihong.Power system transient stability assessment based on comprehensive SVM classification model and key sample set[J].Power System Protection and Control,2017,45(22):1-8.
[8]黄彦浩,于之虹,谢昶,等.电力大数据技术与电力系统仿真计算结合问题研究[J].中国电机工程学报,2015,35(1):13-22.Huang Yanhao,Yu Zhihong,Xie Chang,et al.Study on the application of electric power big data technology in power system simulation[J].Proceedings of the CSEE,2015,35(1):13-22.
[9]薛禹胜,赖业宁.大能源思维与大数据思维的融合(一)大数据与电力大数据[J].电力系统自动化,2016,40(1):1-8.Xue Yusheng,Lai Yening.Integration of macro energy thinking and big data thinking part one big data and power big data[J].Automation of Electric Power Systems,2016,40(1):1-8.
[10]薛禹胜,赖业宁.大能源思维与大数据思维的融合(二)应用及探索[J].电力系统自动化,2016,40(8):1-13.Xue Yusheng,Lai Yening.Integration of macro energy thinking and big data thinking part two applications and explorations[J].Automation of Electric Power Systems,2016,40(8):1-13.
[11]Shyh-Jier H,Jeu-Min L.Enhancement of anomalous data mining in power system predicting-aided state estimation[J].IEEE Transactions on Power Systems,2004,19(1):610-619.
[12]姚德全,贾宏杰,赵帅.基于复合神经网络的电力系统暂态稳定评估和裕度预测[J].电力系统自动化,2013,37(20):41-46.Yao Dequan,Jia Hongjie,Zhao Shuai.Power system transient stability assessment and stability margin prediction base on compound neural network[J].Automation of Electric Power Systems,2013,37(20):41-46.
[13]叶圣永,王晓茹,刘志刚,等.基于支持向量机增量学习的电力系统暂态稳定评估[J].电力系统自动化.2011,35(11):15-19.Ye Shengyong,Wang Xiaoru,Liu Zhigang,et al.Power system transient stability assessment base on support vector machine incremental learning method[J].Automation of Electric Power Systems,2011,35(11):15-19.
[14]Zheng C,Malbasa V,Kezunovic M.Regression tree for stability margin prediction using synchrophasor meas-urements[J].IEEE Transactions on Power Systems,2013,28(2):1978-1987.
[15]Rovnyak S,Kretsinger S,Thorp J,et al.Decision trees for real-time transient stability prediction[J].IEEETransactions on Power Systems,1994,9(3):1417-1426.
[16]Diao R,Vittal V,Logic N.Design of a real-time security assessment tool for situational awareness enhancement in modern power systems[J].IEEETransactions on Power Systems,2010,25(2):957-965
[17]王康,孙宏斌,张伯明,等.基于二维组合属性决策树的暂态稳定评估[J].中国电机工程学报,2009,29(增刊1):17-24.Wang Kang,Sun Hongbin,Zhang Boming,et al.Transient stability assessment based on 2D combined at-tribute decision tree[J].Proceedings of the CSEE,2009,29(S1):17-24.
[18]Xu Yan,Dong Zhaoyang,Zhang Rui,et al.A decision tree-based on-line preventive control strategy for power system transient instability prevention[J].International Journal of Systems Science,2014,45(2):176-186.
[19]Genc I,Diao R,Vittal V,et al.Decision tree-based preventive and corrective control applications for dynamic security enhancement in power systems[J].IEEETransactions on Power Systems,2010,25(3):1611-1619.
[20]Gao Qun,Rovnyak S M.Decision trees using synchronized phasor measurements for wide-area response-based control[J].IEEE Transactions on Power Systems,2011,26(2):855-861.
[21]孙宏斌,王康,张伯明,等.采用线性决策树的暂态稳定规则提取[J].中国电机工程学报.2011,31(34):61-67.Sun Hongbin,Wang Kang,Zhang Boming,et al.Rule extraction in transient stability study using linear decision trees[J].Proceedings of the CSEE,2011,31(34):61-67.
[22]He Miao,Zhang Junshan,Vittal V.Robust online dynamic security assessment using adaptive ensemble decision-tree learning[J].IEEE Transactions on Power Systems,2013,28(4):4089-4098.
[23]Han Jiawei,Kamber M,Pei Jian.数据挖掘概念与技术[M].范明,孟小峰,译.北京:机械工业出版社,2012.
[24]Welling M.Fisher linear discriminant analysis[J]Department of Computer Science,2009,16(94):237-280.
[25]孙静.面向情报大数据的决策树增量学习算法研究[D].哈尔滨:哈尔滨工程大学,2017.
[26]Hiskens I.IEEE PES task force on benchmark systems for stability controls[R].Technical Report,2013.
[27]黄彦浩,于之虹,史东宇,等.基于海量在线历史数据的大电网快速判稳策略[J].中国电机工程学报,2016,36(3):596-603.Huang Yanhao,Yu Zhihong,Shi Dongyu,et al.Strategy of huge electric power system stability quick judgment based on massive historical online data[J].Proceedings of the CSEE,2016,36(3):596-603.
[28]韦肖燕,李欣然,钱军,等.采用储能电源辅助的暂态稳定紧急控制方法[J].电工技术学报,2017,32(18):286-300.Wei Xiaoyan,Li Xinran,Qian Jun,et al.Power system transient stability emergency control method assisted by energy storage[J].Transactions of China Electrotechnical Society,2017,32(18):286-300.
[29]唐飞,贾骏,刘涤尘,等.一种考虑发电机同调分群的大电网快速主动解列策略[J].电工技术学报,2016,31(17):32-40.Tang Fei,Jia Jun,Liu Dichen,et al.A fast active islanding scheme considering generators'coherent partition[J].Transactions of China Electrotechnical Society,2016,31(17):32-40.